Mendelian randomization (MR) is a natural experimental design based on the random transmission of genes from parents to offspring. However, this inferential basis is typically only implicit or used as an informal justification. As parent-offspring data becomes more widely available, we advocate a different approach to MR that is exactly based on this natural randomization, thereby formalizing the analogy between MR and randomized controlled trials. We begin by developing a causal graphical model for MR which represents several biological processes and phenomena, including population structure, gamete formation, fertilization, genetic linkage, and pleiotropy. This causal graph is then used to detect biases in population-based MR studies and identify sufficient confounder adjustment sets to correct these biases. We then propose a randomization test in the within-family MR design using the exogenous randomness in meiosis and fertilization, which is extensively studied in genetics. Besides its transparency and conceptual appeals, our approach also offers some practical advantages, including robustness to misspecified phenotype models, robustness to weak instruments, and elimination of bias arising from population structure, assortative mating, dynastic effects, and horizontal pleiotropy. We conclude with an analysis of a pair of negative and positive controls in the Avon Longitudinal Study of Parents and Children. The accompanying R package can be found at https://github.com/matt-tudball/almostexactmr.

翻译：孟德尔随机化（MR）是一种基于基因从父母到后代随机传递的自然实验设计。然而，这一推断基础通常仅被隐含使用或作为非正式论证。随着亲代-子代数据的日益普及，我们倡导一种不同的MR方法，该方法严格基于这种自然随机化，从而形式化MR与随机对照试验之间的类比。我们首先建立一个表示多种生物学过程和现象的MR因果图模型，包括群体结构、配子形成、受精、遗传连锁和基因多效性。随后利用该因果图检测基于人群的MR研究中的偏倚，并确定纠正这些偏倚的充分混杂因素调整集。接着，我们提出一种基于减数分裂与受精过程中外生随机性的家系内MR设计随机化检验——该随机性在遗传学中已有深入研究。除其透明性与概念吸引力外，我们的方法还具备实际优势：包括对错误指定的表型模型、弱工具变量的稳健性，以及消除由群体结构、选型交配、代际效应和水平多效性引起的偏倚。最后，我们通过分析埃文亲子纵向研究中的一组阳性和阴性对照进行验证。配套R包详见https://github.com/matt-tudball/almostexactmr。